# IMDb Movie Review Sentiment Analysis: A Complete NLP Practice from Text Cleaning to Hybrid Models

> This article details Harshil1335's IMDb movie review sentiment analysis project, which demonstrates a complete NLP processing pipeline including data cleaning, TF-IDF feature extraction, comparison of multiple machine learning models, and an innovative hybrid model of Logistic Regression and SVM, achieving an approximate 89% classification accuracy.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-09T08:56:08.000Z
- 最近活动: 2026-05-09T09:00:48.544Z
- 热度: 145.9
- 关键词: 自然语言处理, NLP, 情感分析, 文本分类, TF-IDF, 机器学习, 逻辑回归, SVM, 朴素贝叶斯, IMDb数据集
- 页面链接: https://www.zingnex.cn/en/forum/thread/imdb-nlp
- Canonical: https://www.zingnex.cn/forum/thread/imdb-nlp
- Markdown 来源: floors_fallback

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## [Introduction] Overview of the IMDb Sentiment Analysis Project

The open-source imdb-sentiment-analysis-nlp project by GitHub user Harshil1335 demonstrates a complete NLP sentiment analysis pipeline, covering data cleaning, TF-IDF feature extraction, comparison of multiple machine learning models, and an innovative hybrid model. It achieves an approximate 89% accuracy on the IMDb movie review dataset, providing a reproducible learning example for NLP beginners.

## Project Background and Dataset Introduction

The project aims to automatically identify the sentiment tendency of movie reviews (binary classification: positive/negative). It uses the classic IMDb movie review dataset: total samples 25309, with 50% positive and 50% negative reviews; 80% for training (20247 samples) and 20% for testing (5062 samples). The balanced distribution ensures no class bias in the model.

## Text Preprocessing and Feature Engineering Methods

**Text Preprocessing**: Completed via the NLTK library, including lowercase conversion, tokenization, removal of HTML tags/punctuation/special characters, and filtering stopwords (e.g., "the", "is") to focus on sentiment words.
**Feature Engineering**: Compared the Bag-of-Words model (simple but ignores word order) with TF-IDF (term frequency + inverse document frequency, identifies high-value words), using a 10000-dimensional TF-IDF feature space to balance information and complexity.

## Model Performance Comparison and Experimental Evidence

Trained and evaluated four algorithms:
1. Naive Bayes: Bag-of-Words (84.67%), TF-IDF (86.59%)
2. Linear SVM (TF-IDF): 88.34%
3. Logistic Regression (TF-IDF): 88.96% (best single model)
4. Hybrid Model (Logistic Regression + SVM): 88.82%, MCC 0.7764, FDR 0.1155, with balanced and reliable performance.

Result table:
| Algorithm | Feature | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|
| Naive Bayes | Bag-of-Words | 84.67% | 0.85 | 0.85 | 0.85 |
| Naive Bayes | TF-IDF | 86.59% | 0.87 | 0.87 | 0.87 |
| Linear SVM | TF-IDF | 88.34% | 0.88 | 0.88 | 0.88 |
| Logistic Regression | TF-IDF | 88.96% | 0.89 | 0.89 | 0.89 |
| Hybrid Model | TF-IDF | 88.82% | 0.89 | 0.89 | 0.89 |

## Summary of Experimental Results and Key Findings

1. **Feature Quality First**: TF-IDF improves accuracy by 2-3 percentage points compared to Bag-of-Words; good features are more important than complex models.
2. **Linear Models Are Efficient**: Linear models like SVM and Logistic Regression perform excellently; non-linear models are not necessary.
3. **Ensemble Strategy Is Effective**: Although the hybrid model does not outperform the best single model, its performance is more balanced.

## Application Value and Expansion Directions

**Application Value**: Provides a complete learning example for NLP beginners covering text preprocessing, feature engineering, and model evaluation.
**Expansion Directions**: Multilingual sentiment analysis, fine-grained rating prediction, aspect-level sentiment analysis, real-time review monitoring API deployment, etc.